Session-based Recommender Systems (SBRSs) aim at timely predicting the next likely item by capturing users’ current preferences in sessions. Existing SBRSs research only focuses on maximizing session utilities, and little has been done on the ...

Meta learning has been recognized as an effective remedy for solving the cold-start problem in the recommendation domain. Existing models aim to learn how to generalize from the user behaviors in the training set to testing set. However, in the cold start ...

Social recommendations leverage social networks to augment the performance of recommender systems. However, the critical task of denoising social information has not been thoroughly investigated in prior research. In this study, we introduce a ...

Attributed network embedding seeks to depict each network node via a compact, low-dimensional vector while effectively preserving the similarity between node pairs, which lays a strong foundation for a great many high-level network mining tasks. With the ...

Anomalies often occur in real-world information networks/graphs, such as malevolent users in online review networks and fake news in social media. When representing such structured network data as graphs, anomalies usually appear as anomalous nodes that ...

Amidst the rapid propagation of multimodal fake news across social media platforms, the detection of fake news has emerged as a prime research pursuit. To detect heightened level of meticulous fabrications, propagation paths are introduced to provide ...

Many real-world networks are signed networks with positive and negative edge weights, such as social networks with positive (friend) or negative (foe) relationships between users, and gene interaction networks with positive (stimulatory) or negative (...

Multi-modal contents have proven to be the powerful knowledge for recommendation tasks. Most state-of-the-art multi-modal recommendation methods mainly focus on aligning the semantic spaces of different modalities to enhance the item representations and ...

Collaborative filtering (CF) models have demonstrated remarkable performance in recommender systems, which represent users and items as embedding vectors. Recently, due to the powerful modeling capability of graph neural networks for user-item interaction ...

This paper explores explaining session-based recommendation (SR) by path reasoning. Current SR models emphasize accuracy but lack explainability, while traditional path reasoning prioritizes knowledge graph exploration, ignoring sequential patterns ...

Training recommendation models on large datasets requires significant time and resources. It is desired to construct concise yet informative datasets for efficient training. Recent advances in dataset condensation show promise in addressing this problem ...

Learning representations from pairwise comparisons has achieved significant success in various fields, including computer vision and information retrieval. In recommendation systems, collaborative filtering algorithms based on pairwise learning are also ...

With the rapid expansion of multimodal content in online social media, automatic detection of multimodal fake news has received much attention. Multimodal joint training commonly used in existing methods is expected to benefit from thoroughly leveraging ...

Sequential recommendation is a critical part of the flourishing online applications by suggesting appealing items on users’ next interactions, where global dependencies among items have proven to be indispensable for enhancing the quality of item ...

In recommendation systems, Graph Convolutional Networks (GCNs) often suffer from significant computational and memory cost when propagating features across the entire user-item graph. While various sampling strategies have been introduced to reduce the ...

Information cascade popularity prediction is an important problem in social network content diffusion analysis. Various facets have been investigated (e.g., diffusion structures and patterns, user influence) and, recently, deep learning models based on ...

Cross-Domain Recommendation has been popularly studied to resolve data sparsity problem via leveraging knowledge transfer across different domains. In this paper, we focus on the <italic>Unified Cross-Domain Recommendation</italic> (<italic>Unified CDR</...

Many large-scale machine learning approaches and graph algorithms are proposed recently to address a variety of problems in online social networks (OSNs). To evaluate and validate these algorithms and models, the data of ego-centric networks (ego networks)...

Sequential recommendation is a critical but challenging task in capturing users&#x2019; potential preferences due to inherent biases in the data. Existing debiasing recommendation methods aim to eliminate biases from historical interaction data collected ...

A signed graph offers richer information than an unsigned graph, since it describes both collaborative and competitive relationships in social networks. In this paper, we study opinion dynamics on a signed graph, based on the Friedkin-Johnsen model. We ...